package com.zck.aiagent.app;

import com.zck.aiagent.advisor.MyLoggerAdvisor;
import com.zck.aiagent.chatmemory.DatabaseChatMemory;
import com.zck.aiagent.chatmemory.FileBasedChatMemory;
import jakarta.annotation.Resource;
import lombok.extern.slf4j.Slf4j;
import org.springframework.ai.chat.client.ChatClient;
import org.springframework.ai.chat.client.advisor.MessageChatMemoryAdvisor;
import org.springframework.ai.chat.client.advisor.QuestionAnswerAdvisor;
import org.springframework.ai.chat.client.advisor.api.Advisor;
import org.springframework.ai.chat.memory.ChatMemory;
import org.springframework.ai.chat.memory.InMemoryChatMemory;
import org.springframework.ai.chat.model.ChatModel;
import org.springframework.ai.chat.model.ChatResponse;
import org.springframework.ai.vectorstore.VectorStore;
import org.springframework.stereotype.Component;

import java.util.List;

import static org.springframework.ai.chat.client.advisor.AbstractChatMemoryAdvisor.CHAT_MEMORY_CONVERSATION_ID_KEY;
import static org.springframework.ai.chat.client.advisor.AbstractChatMemoryAdvisor.CHAT_MEMORY_RETRIEVE_SIZE_KEY;

@Slf4j
@Component
public class LoveApp {

    private final ChatClient chatClient;

    private static final String SYSTEM_PROMPT = "扮演深耕恋爱心理领域的专家。开场向用户表明身份，告知用户可倾诉恋爱难题。" +
            "围绕单身、恋爱、已婚三种状态提问：单身状态询问社交圈拓展及追求心仪对象的困扰；" +
            "恋爱状态询问沟通、习惯差异引发的矛盾；已婚状态询问家庭责任与亲属关系处理的问题。" +
            "引导用户详述事情经过、对方反应及自身想法，以便给出专属解决方案。";
//    @Value("classpath:/prompts/system-message.st")
//    private Resource systemResource;


    /**
     * 初始化ChatClient
     * @param dashscopeChatModel
     */
    public LoveApp(ChatModel dashscopeChatModel,DatabaseChatMemory databaseChatMemory) {
        // 初始化基于内存的对话记忆
        ChatMemory chatMemory = new InMemoryChatMemory();
        //初始化基于文件的对话记忆
        String fileDir = System.getProperty("user.dir") + "/chat-memory";
        ChatMemory fileBasedChatMemory = new FileBasedChatMemory(fileDir);

        //初始化基于mysql的对话记忆

        chatClient=ChatClient.builder(dashscopeChatModel)
                .defaultSystem(SYSTEM_PROMPT)
                .defaultAdvisors(
                        new MessageChatMemoryAdvisor(databaseChatMemory),
                        // 自定义日志Advisor,可按需开启
                        new MyLoggerAdvisor()
                        // 自定义提示词重读
                        // new ReReadingAdvisor()
                        //自定义过滤违禁词(1.0.0.2里面有SafeGuardAdvisor是做在用户输入中检测敏感词，并在发现敏感词时阻止调用模型并返回预设的失败响应
                        //敏感词匹配规则，实现设置一系列敏感词列表，校验提示词中是否包含敏感词)
                        //new MyViolationProhibitionAdvisor()
                        )
                .build();
    }

    public String doChat(String message, String chatId) {
        ChatResponse response = chatClient
                .prompt()
                .user(message)
                .advisors(spec -> spec.param(CHAT_MEMORY_CONVERSATION_ID_KEY, chatId)
                        .param(CHAT_MEMORY_RETRIEVE_SIZE_KEY, 10))
                .call()
                .chatResponse();
        String content = response.getResult().getOutput().getText();
//        log.info("content: {}", content);
        return content;
    }

    public String doChat(String message) {
        ChatResponse response = chatClient
                .prompt()
                .user(message)
                .advisors(spec ->
                        spec.param(CHAT_MEMORY_RETRIEVE_SIZE_KEY, 10))
                .call()
                .chatResponse();
        String content = response.getResult().getOutput().getText();
//        log.info("content: {}", content);
        return content;
    }

    public LoveReport doChatWithReport(String message, String chatId) {
        LoveReport loveReport = chatClient
                .prompt()
                .system(SYSTEM_PROMPT + "每次对话后都要生成恋爱结果，标题为{用户名}的恋爱报告，内容为建议列表")
                .user(message)
                .advisors(spec -> spec.param(CHAT_MEMORY_CONVERSATION_ID_KEY, chatId)
                        .param(CHAT_MEMORY_RETRIEVE_SIZE_KEY, 10))
                .call()
                .entity(LoveReport.class);
        log.info("loveReport: {}", loveReport);
        return loveReport;
    }


    record LoveReport(String title, List<String> suggestions) {}

    @Resource
    private VectorStore loveAppVectorStores;

    @Resource
    private Advisor loveAppRagCloudAdvisor;

    public String doChatWithRag(String message, String chatId) {
        ChatResponse chatResponse = chatClient
                .prompt()
                .user(message)
                .advisors(spec -> spec.param(CHAT_MEMORY_CONVERSATION_ID_KEY, chatId)
                        .param(CHAT_MEMORY_RETRIEVE_SIZE_KEY, 10))
                //应用知识库问答
//                .advisors(new QuestionAnswerAdvisor(loveAppVectorStores))
                .advisors(loveAppRagCloudAdvisor)
                .call()
                .chatResponse();
        String content = chatResponse.getResult().getOutput().getText();
        log.info("loveReport: {}", content);
        return content;

    }

    /**
     * 推荐对象
     * @param message
     * @param chatId
     * @return
     */
    @Resource
    private VectorStore loveAppVectorStore;
    public String doChatWithRagtest(String message, String chatId) {
        ChatResponse chatResponse = chatClient
                .prompt()
                .user(message)
                .advisors(spec -> spec.param(CHAT_MEMORY_CONVERSATION_ID_KEY, chatId)
                        .param(CHAT_MEMORY_RETRIEVE_SIZE_KEY, 10))
                //应用知识库问答
                .advisors(new QuestionAnswerAdvisor(loveAppVectorStore))
//                .advisors(loveAppRagCloudAdvisor)
                .call()
                .chatResponse();
        String content = chatResponse.getResult().getOutput().getText();
        log.info("loveReport: {}", content);
        return content;

    }

}




